Hello,
I want to make sure I understand this correctly.
arch=resnet34
data = ImageClassifierData.from_paths(PATH, tfms=tfms_from_model(arch, sz))
learn = ConvLearner.pretrained(arch, data, precompute=True)
learn.fit(0.01, 2)
First the model is trained on the training set, the accuracy for each epoch is the comparison of predictions from validation set with the actual validation values.
What is the point of learn.predict on the validation set lesson 1? I know learn.predict is used for a test set but does this mean the predictions used to determine accuracy are not stored in the learn object and have to be made again? Will the predictions have different values than the ones obtained when determining accuracy?